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Work Package 4 „Workshop on Observation errors “ Vienna. Leopold Haimberger. Overview 19 April 2012. WP4 Objectives. Ensure optimal use of observations in reanalysis Provide data monitoring tools to support uncertainty assessments
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Work Package 4 „Workshop on Observation errors“Vienna Leopold Haimberger Overview 19 April 2012
WP4 Objectives • Ensure optimal use of observations in reanalysis • Provide data monitoring tools to support uncertainty assessments • Detect inconsistencies in and among input datasets employing departure statistics from (pilot) reanalyses • Reduce uncertainties in reanalysis through bias correction of input • Explore opportunities for variational bias correction of selected in-situ observation types • Improvement of ocean observations for reanalysis • Provide consistent and meaningful error bounds • for all data input to reanalysis • employing departure statistics
WP4 contributors • ECMWF: OFA interface, (variational) biasestimation • UKMET: Oceanobservations, earlysatellites • UBERN: Upperairdatabiascorrection • EUMST: Satellitedataerrorestimates • UNIVIE: Offline/online upperairdatabiasestimation, WP4 lead • RIHMI: Homogenizationof time series • FFCUL: Pressuredatabiascorrection
WP4 Workshop in Vienna • Discuss uncertainty estimation methods • Methods for quantifying observation errors • Bias estimation and correction of observations • Impact of observation errors on reanalysis • Impact of uncertainties of surface boundary conditions on reanalysis • Methods for estimating and reducing the uncertainty of reanalysis products • Choose quantities to be stored in observation archive, specify an archive plan for uncertainty estimates • 19-20 April 2012
In situ upperairbiascorrectionactivitiesat Univ. Vienna Leopold Haimberger, Marco Milan, Lorenzo Ramella-Pralungo, Christina Tavolato 19 April 2012
Outline • Mainly on T adjustmentsfrom 1958 onwards • RAOBCORE/RICH homogenizationsystem • Somediagnosticsoftheadjustmentsystem • Breaksize estimation • Adjustmentensembles, sensitivityexperiments • Comparisonwithsatellitedata • Other parametersthan T • Back toearly 20th century ->Lorenzo R. • Variationalbiascorrectionofradiosondes • Wind direction -> Christina T. • Temperature -> Marco M.
Observation Feedback • Background (y-Hxb) andanalysisdeparturestatisticsfrompilotassimilationsandreanalyses • Credo: Departurestatisticshave high potential for QC/BC • So farweuseddeparturestatisticsfrom • ERA-Interim (ODB files) • ERA-40 (BUFR files) • IGRA (bgcalculated „offline“ frominterpolatedgriddedbgfields) • CHUAN v1 (bgcalculated offline, z-level wind datainterpolatedto p-levels) • Departurestatisticsfrom 20th Century Reanalyses v2 • Obs fromabovearchives • Departuresfrominterpolatedensemblemeananalysis(sinceanalysisindependentof RS data)
Obs-bg(merged ERA-40/ERA-Interim) RecentShifts Annual cycleofbias Shifts
Homogenisationmethods • RAOBCORE „Radiosonde Observation CorrectionusingReanalysis“ • Detectsinhomogeneities in observationrecordsfromy-H(x) (obs-bg) time series • Obs-bg time series also usedforobsadjustment • 1100 Stations, back to 1958 • RICH „Radiosonde Innovation Composite Homogenization“ • Relies on breakpointsdetectedby RAOBCORE but usesneighboringrecordsforadjustmentestimation • RICH-obscompares 10-30 neighboringobsrecords • RICH-t compares 10-30 neighboringbg-obsrecords
A breaksizeestimationexample Bethel, Alaska, 198906 1st Iteration (9 neighbors) 2nd Iteration (30 neighbors)
Standard errorof sample means RAOBCORE: Meansof Background departures, RICH-obs: Meansofdifferencebetweentwostationrecords RICH-tau: Meansofdifferencebetweenbg-obsoftwostations Stddev (K)
Differencebetween RAOBCORE and RICH breaksizeestimates • This differenceis larger thanestimatesabove, • likely due toundetectedbiases in thereferenceseries • due totoolittledatafor RICH mainlyat high altitudes
Station climatology adjustment Mean T of neighbouring homogenized „reliable“ series actual (bg-obs) T bg Mean T of most recent part of tested series Expected (bg-obs) (bg-obs)i obsi Bg used for interpolation x,y
Can webelieve in theadjustedvalues? • Improvedspatiotemporalconsistencyisonlynecessary but not sufficientfor temporal homogeneity. • How sensitive areresultstovariationofuncertainparameters in theadjustmentsystem? • Are adjustedseriesconsistentwithsimilardatasets?
Parameters oftheadjustmentsystem • Break detectionefficiency(Howmanybreaks) • Selectionofneighbors (Howmany) • Weightingofneighborswithdistance • Minimum numberofgoodvalues • RICH-obsor RICH-tau • Choice ofparameters, rangessubjective • Conditionof high spatiotemporalconsistencyoftrendslimitsspreadofmeantrends • See Haimberger et al. (2012, rev, JC) • Not yet a probabilisticapproach but a start
Ensemble of RICH adjustments Haimberger et al. 2011, subm. to JC RICH-tau RICH-obs RAOBCORE ERA40/Interim an Unadjusted Spreadthroughvariationofparameters in RICH, e.g. numberofneighbors, weightingwithdistance, minimumrequiredgoodvaluesforadjustment
Removalofsignalexperiments • Shiftsintroduced in all time series such thatclimatesignal (blueline) isreducedtozero • RAOBCORE/RICH canrecoverclimatesignal in thetropicsifbreakpointsareknown
Tropical temperaturevariability Santer et al. (2005), Science RATPAC HadAT AR4 climatemodels Temperaturevariability Trends, 1=0.12K/10a
Lowertroposphericseries, Tropics Fullseries Black is HadCRUT3 Differenceseries
Adjustmentof ERA-40 backgroundTransition from ERA-40 to ERA-Interim in 1979 coincideswith FGGE, Satelliteintroduction. MakesestimateofshiftdifficultUncertaintyDT ~0.1-0.5K
Lowerstratosphericseries, Tropics Fullseries Differenceseries
Upperair wind trends? Vautard et al. Nature 2010 850 hPa
Wind speedbiases Obs-bg, USA/Canada composite
To Do List • Temperatureadjustmentswithannualcycle • Adjustments back to pre-1958 forbothtemperatureand wind usingsurfacedataonlyreanalyses • Offline homogenizationof wind datatocomplementvariationalapproach • Adjustmentdatabaseforconventionalbiascorrection, breakpointdatabasetosupportvariationalbiascorrection